↓ Skip to main content

SAMSA2: a standalone metatranscriptome analysis pipeline

Overview of attention for article published in BMC Bioinformatics, May 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
72 tweeters

Citations

dimensions_citation
31 Dimensions

Readers on

mendeley
196 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
SAMSA2: a standalone metatranscriptome analysis pipeline
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2189-z
Pubmed ID
Authors

Samuel T. Westreich, Michelle L. Treiber, David A. Mills, Ian Korf, Danielle G. Lemay

Abstract

Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution. SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.

Twitter Demographics

The data shown below were collected from the profiles of 72 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 196 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 26%
Researcher 33 17%
Student > Master 26 13%
Student > Doctoral Student 17 9%
Student > Bachelor 11 6%
Other 21 11%
Unknown 38 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 27%
Biochemistry, Genetics and Molecular Biology 32 16%
Environmental Science 19 10%
Immunology and Microbiology 16 8%
Computer Science 10 5%
Other 16 8%
Unknown 50 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 35. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 September 2019.
All research outputs
#678,726
of 16,651,917 outputs
Outputs from BMC Bioinformatics
#67
of 5,985 outputs
Outputs of similar age
#20,833
of 284,431 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 22 outputs
Altmetric has tracked 16,651,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,985 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 284,431 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.